Analysis of the role of Global Value Chains (GVC) in accessing knowledge and enhancing learning and innovation. Global Value Chains, Innovation Systems, Governance, Foreign Direct Investment, Learning, Upgrading, Productivity. Three main conclusions emerge from the analytical framework and evidence presented in this paper.
Agricultural education, research, and extension can contribute substantially to reducing rural poverty in the developing world. However, evidence suggests that their contributions are falling short in Sub-Saharan Africa. The entry of new actors, technologies, and market forces, when combined with new economic and demographic pressures, suggests the need for more innovative and less linear approaches to promoting a technological transformation of smallholder agriculture.
Historically, farmers have been some of the most innovative people in the world. However, agriculture lags behind other sectors in its uptake of new information technologies for the control and automation of farming systems. In spite of decades of research into innovation, generally we still do not have a good understanding as to why this is the case. This paper reviews two theories of innovation and offers a new approach to thinking about agricultural ICT (e-Agriculture). It firstly explores the problem of ICT adoption in agriculture.
This paper is an attempt to take stock of the authors' work. In Section 2, the authors reflect upon the emergence and fairly rapid diffusion of the concept ‘national system of innovation’ as well as related concepts. In Section 3, they describe how the Aalborg-version of the concept evolved by a combination of ideas that moved from production structure towards including all elements and relationships contributing to innovation and competence building.
This paper reviews the NIS literature chronologically, showing how this shift in emphasis has diminished somewhat the importance of both institutions, particularly governments, and the political processes of institutional capacity building.
Processes of designing for systemic innovation for sustainable development (SD) through the lens of three long-term case studies are reported. All case studies, which originated from the SLIM (Social Learning for the Integrated Management and Sustainable Use of Water at Catchment Scale) Project, funded within the EU Fifth Framework Program (2001–2004), constitute inquiry pathways that are explored using a critical incident approach.
There is an ongoing debate on what constitutes sustainable intensification of agriculture (SIA). In this paper, we propose that a paradigm for sustainable intensification can be defined and translated into an operational framework for agricultural development. We argue that this paradigm must now be defined—at all scales—in the context of rapidly rising global environmental changes in the Anthropocene, while focusing on eradicating poverty and hunger and contributing to human wellbeing.
As calls for bolstering environmental services on croplands have grown more insistent during the past two decades, the search for ways to foster sustainable, reduced input agriculture has become more urgent. In this context authors re-examine by means of a meta-analysis the argument, first proposed by Robert McC. Netting, that small scale, mixed crop – livestock farming, a common livelihood among poor rural peoples, encourages environmentally sustainable agricultural practices.
The development of effective agricultural monitoring networks is essential to track, anticipate and manage changes in the social, economic and environmental aspects of agriculture. The authors welcome the perspective of Lindenmayer and Likens (J. Environ. Monit., 2011, 13, 1559) as published in the Journal of Environmental Monitoring on their earlier paper, “Monitoring the World's Agriculture” (Sachs et al., Nature, 2010, 466, 558–560).
One of the most important challenges for the researchers in the 21st Century is related to global heating and climate change that can have as consequence the intensification of natural hazards. Another problem of changes in the Earth's climate is its impact in the agriculture production. In this scenario, application of statistical models as well as development of new methods become very important to aid in the analyses of climate from ground-based stations and outputs of forecasting models. Additionally, remote sensing images have been used to improve the monitoring of crop yields.